UA Decision Systems
Turn messy UA budget logic into decision systems
I help paid UA and growth teams formalize how they actually make weekly decisions — so that signal rights, decision trees, recommendation rules, and agent-safe automation can be built on top.
9-block scorecard · takes ~10 min · no login required
The problem
AI can now act on your ad budget. But does it know when it's allowed to?
Paid UA teams are moving toward AI, automation, and recommendation systems — but the decision logic behind scale, hold, cut, and reallocate is often still informal. It lives in people's heads, not in specs.
For AI-agent teams
AI agents can access ad accounts, move budgets, and pause campaigns. Without signal rights and guardrails, they act on immature signals — fast, automatically, and at scale.
For teams building automation
Recommendation systems and decision tools inherit the logic of whoever built them. If that logic was never formalized, automation inherits the gaps — and the errors.
Metric movement ≠ permission
Trials grew. ROAS dropped. CPI spiked. These are observations, not execution signals. The question is: is this signal mature enough to act on?
Subscription apps: extra risk
Spend is visible today. LTV, refunds, and renewals arrive later. AI can act now — but the economics only reveal themselves after the cohort matures.
The approach
Decision logic before automation
Before AI agents or recommendation systems can work reliably, the team needs to formalize how UA decisions are actually made — week after week.
01
Decision Inventory
Map every recurring UA decision: scale, hold, cut, protect, investigate. Name triggers, owners, and frequency.
02
Signal Rights
Classify each signal: budget-ready, diagnostic-only, or immature. Define what each signal is allowed to trigger.
03
Trust Checks
Before acting, verify: cohort maturity, attribution reliability, source-of-truth clarity, plan mix stability.
04
Decision Trees
Convert UA manager logic into documented, testable decision trees — validated against historical decisions.
05
Guardrails
Define budget change limits, minimum cohort sizes, and forbidden actions per signal type.
06
Human Approval + Loop
Specify which decisions require human approval. Build outcome review so rules improve over time.
decision inventory
→
signal rights
→
trust checks
→
decision trees
→
guardrails
→
human approval
→
learning loop
Who this is for
Three types of teams
Segment
How they think about it
Entry point
Subscription apps
with paid UA
Signals mature slowly. AI can act too fast on early ROAS, trials, or pLTV before the cohort is ready.
AI Ads Decision Safety · Signal rights before execution
Gaming & app companies
with UA automation
Building recommendation systems or automation tools. Need formalized decision logic, not just dashboards.
UA Decision Systems · Decision trees & recommendation rules
Performance agencies
managing client budgets
AI may touch client ad accounts. Need guardrails and clear human approval rules before that happens.
Client-safe AI · Human approval workflows
Offer
What I help with
One core engagement, designed to close the gap between informal UA intuition and production-ready decision logic.
UA Decision Logic Sprint
2 weeks
From messy UA manager logic to validated recommendation rules and production-ready specs — built around one real recurring decision your team makes every week.
Outputs
→ Decision inventory
→ Signal rights map
→ Source-of-truth gaps
→ Decision tree / recommendation rules
→ Guardrails & human approval logic
→ Next automation roadmap
Good fit if you are
— Building a recommendation system for UA
— Connecting AI agents to ad accounts
— Automating weekly budget decisions
— Formalizing UA logic before scaling a team